A New Multiple Imputation Method for High-Dimensional Neuroimaging Data.

ABSTRACT: Missing data are a prevalent challenge in neuroimaging, with significant implications for downstream statistical analysis. Neglecting this issue can introduce bias and lead to erroneous inferential conclusions, making it crucial to employ appropriate statistical methods for handling missing data. Although the multiple imputation is a widely used technique, its application in neuroimaging is severely hindered by the high dimensionality of neuroimaging data, and the substantial computational demands. To tackle the critical computational challenges, we propose a novel approach, High dimensional Multiple Imputation (HIMA), based on Bayesian models specifically designed for large-scale neuroimaging datasets. HIMA introduces a new computational strategy to sample large covariance matrices based

SUBMITTER: Lu T 

PROVIDER: S-EPMC11926575 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature